How to Estimate the Cost of Running SaaS-Based vs. Open Source LLM Models

If you’re building an AI-powered product—especially something like a chatbot, research assistant, or content generation tool—choosing between a SaaS-based LLM (like OpenAI, Anthropic, or Cohere) and running your own open-source LLM (like LLaMA, Mistral, or Falcon) is a major decision.

Each approach has trade-offs in cost, performance, control, scalability, and security. But let’s focus on what most startups and teams care about first: COST.

In this post, we’ll break down how to estimate the cost of both approaches so you can make an informed decision.

💸 Option 1: SaaS-Based LLM (API Usage)

SaaS-based LLMs are cloud-hosted models provided by companies like:

OpenAI (GPT-3.5, GPT-4-turbo)
Anthropic (Claude)
Google (Gemini via Vertex AI)
Cohere, AI21, etc.

🧮 How to Estimate SaaS LLM Cost

You're billed per token (input + output). Each provider has different pricing, so here’s an example using OpenAI's GPT-4-turbo:

Model	Price / 1,000 Tokens	Example
GPT-3.5-turbo	$0.0015 input / $0.002 output	1,000 tokens = ~$0.004
GPT-4-turbo	$0.01 input / $0.03 output	1,000 tokens = ~$0.04

🔍 Estimating Monthly Usage

Let’s say you run a research-writing assistant SaaS and estimate:

50,000 users/month
Each user generates 10 responses
Each response is ~1,000 tokens total (input + output)

Monthly Tokens:50,000 users × 10 × 1,000 = 500,000,000 tokens

Monthly Cost (GPT-4-turbo):500M ÷ 1,000 × $0.04 = $20,000/month

💡 Pro Tip: Use GPT-3.5 where acceptable, and reserve GPT-4 for premium plans or complex tasks.

🔧 Option 2: Open-Source LLM (Self-Hosted)

Running models like LLaMA 2, Mistral, or Mixtral gives you full control over the system, token limits, privacy, and even customization—but you also bear the infrastructure cost.

🖥️ Key Cost Drivers

GPU Costs (Cloud or On-Prem)
- A single A100 (80GB) instance on AWS costs ~$2–$3/hour
- Multi-GPU (for faster inference or larger models) scales this up fast
- Alternatively, use consumer GPUs (like RTX 4090) for smaller deployments
Inference Optimization
- Use tools like vLLM, TGI, or Text Generation WebUI
- Quantized models (4-bit or 8-bit) reduce memory and GPU requirements
Scalability
- You’ll need load balancers, autoscaling logic, and GPU inference queues
- Consider Replicate, RunPod, or Modal for pay-as-you-go inference
Storage + Network + Maintenance
- Storing model weights, logs, and traffic incurs additional (but smaller) costs
- You also need engineers to manage and monitor the stack

💡 Rough Monthly Cost Breakdown (Open-Source)

Let’s say you run LLaMA 2 13B or Mistral 7B using a single A100:

Component	Cost Estimate
A100 Instance (on-demand, ~730 hrs/month)	~$2,000
Load balancing / autoscaling infra	$200–$500
Engineering time (DevOps/MLOps)	Varies
Total	~$2,500–$5,000/month (starting point)

Want to serve 1M+ users? You’ll need multiple GPUs and inference nodes.

⚖️ SaaS vs. Open Source: Quick Comparison

Feature	SaaS (e.g., OpenAI)	Open Source (e.g., LLaMA)
Upfront Cost	Low	Medium to High
Scaling	Easy (cloud handles it)	Manual or 3rd party
Performance	Best-in-class (GPT-4)	Good, customizable
Control	Limited	Full control
Compliance / Privacy	May be limited (esp. for EU users)	More control
Pricing Model	Pay-per-token	Pay-for-infra

🧠 TL;DR: How to Choose

💼 Startups / MVPs / fast launch → Use SaaS LLMs for speed and simplicity.
🏗️ Custom AI tools, privacy-sensitive data, or high-volume usage → Consider open-source to reduce long-term cost and increase control.
🧪 Hybrid models → Use SaaS for general tasks + open-source for custom domain tasks.

🔍 Bonus: Free & Affordable Options

Groq: Ultra-fast inference of Mixtral (pay-per-request)
OpenRouter.ai: Aggregate LLMs with flexible pricing
Replicate / RunPod: Run open-source LLMs affordably with auto-scaling

Final Thoughts

Estimating cost isn’t just about tokens or GPU hours—it’s about your growth stage, team skills, user base, and product goals. SaaS LLMs are great for getting started fast, while open-source LLMs shine when you need flexibility and scale.